Recent content by PredictorY

Yes, but I think most people want to make as much money as possible in a given span of time. If all you want is to better than zero, I agree that you can buy index funds and wait. Most people here, though, are seeking more than that.

"If you don't know where you are going, any road will get you there." -Lewis Carroll

Situations vary quite a bit, so rather than give a 'use language X' answer, I'll just suggest a few things to consider, in no special order: - Which language(s) are you already most familiar with? Ones you already know, even modestly well, will obviously require less effort to apply. Also...

I would say rather that "A neural network can be badly trained to "predict" all sorts of random stuff." A competent analyst, however, would easily see that this is happening, via error resampling (holdout testing, k-fold cross validation, etc.). An important part of the job of the analyst is to...

Sorry, I should have been more specific. I was trying to make the point that this assertion of yours: "Suppose we have a new input of 2343144. The kNN will simply output 3452452. It won't try to figure out a formula to match all of the data, and then plug this new input into it." ...is...

I have found many different machine learning algorithms to be useful, but I have found that the actual machine learning part of any project has less effect on the quality of the solution than things like data collection or deciding how to apply machine learning to the problem (which is not...

This is true when k = 1, but such models can be extremely noisy: When input values change slightly, the model can swing wildly.

Broadly, neither technique is more likely to overfit than the other. Overfitting (and underfitting, for that matter) may be avoided in any type of empirical model by appropriately establishing model complexity. This is usually tested via error resampling (holdout testing, cross-validation or...